Safe Disassociation of Set-Valued Datasets
| Autor: | Awad, Nancy, Al Bouna, Bechara, Couchot, Jean-François, Philippe, Laurent |
|---|---|
| Přispěvatelé: | Franche-Comté Électronique Mécanique, Thermique et Optique - Sciences et Technologies (UMR 6174) (FEMTO-ST), Université de Technologie de Belfort-Montbeliard (UTBM)-Ecole Nationale Supérieure de Mécanique et des Microtechniques (ENSMM)-Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Centre National de la Recherche Scientifique (CNRS), Université Antonine (UA) |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
[INFO.INFO-IU]Computer Science [cs]/Ubiquitous Computing [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR] Computer Science - Databases [INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA] Data_MISCELLANEOUS Databases (cs.DB) [INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET] [INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE] [INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC] [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation |
| Zdroj: | Journal of Intelligent Information Systems Journal of Intelligent Information Systems, 2019, 53 (3), pp.547-562 |
| Popis: | Disassociation introduced by Terrovitis et al. is a bucketization based anonimyzation technique that divides a set-valued dataset into several clusters to hide the link between individuals and their complete set of items. It increases the utility of the anonymized dataset, but on the other side, it raises many privacy concerns, one in particular, is when the items are tightly coupled to form what is called, a cover problem. In this paper, we present safe disassociation, a technique that relies on partial-suppression, to overcome the aforementioned privacy breach encountered when disassociating set-valued datasets. Safe disassociation allows the $k^m$-anonymity privacy constraint to be extended to a bucketized dataset and copes with the cover problem. We describe our algorithm that achieves the safe disassociation and we provide a set of experiments to demonstrate its efficiency. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|